As we discussed, SSTs [1]} are able to localize all objects in an image. They decompose the objects of an image into different maps, because they learn to represent objects in a self-supervised fashion. Each map highlights different objects or their parts without associating categorical information with them. If we can successfully identify maps and corresponding regions that contain the concerned object, then we can use them as pseudo-labels to train a localization network. Therefore, we propose a new DiPS method that can harvest efficient pseudo-labels on the target object for accurate WSOL. DIPS utilizes class tokens of a pretrained ViT to harvest effective pseudo-labels, and a pretrained CNN classifier to select ROIs proposal to train our localization network (see Fig.REF ).
